SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality

In the last year alone, a surge of new benchmarks to measure $\textit{compositional}$ understanding of vision-language models have permeated the machine learning ecosystem. Given an image, these benchmarks probe a model's ability to identify its associated caption amongst a set of compositional distractors. Surprisingly, we find significant biases in $\textit{all}$ these benchmarks rendering them hackable. This hackability is so dire that blind models with no access to the image outperform state-of-the-art vision-language models. To remedy this rampant vulnerability, we introduce $\textit{SugarCrepe}$, a new benchmark for vision-language compositionality evaluation. We employ large language models, instead of rule-based templates used in previous benchmarks, to generate fluent and sensical hard negatives, and utilize an adversarial refinement mechanism to maximally reduce biases. We re-evaluate state-of-the-art models and recently proposed compositionality inducing strategies, and find that their improvements were hugely overestimated, suggesting that more innovation is needed in this important direction. We release $\textit{SugarCrepe}$ and the code for evaluation at: https: //github. com/RAIVNLab/sugar-crepe.

Authors

• Cheng-Yu Hsieh
• Jieyu Zhang
• Zixian Ma
• Aniruddha Kembhavi
• Ranjay Krishna

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